Technical Abstract: Worldwide, extremes in the hydrologic cycle (i.e. flood and drought) cause billions of dollars in damage to agricultural production and distribution systems every year. Soil moisture - and our ability to monitor it - is a key part of current efforts to reduce these costs. As the critical land surface hydrologic state, soil moisture determines the magnitude of crop water stress during drought and runoff amounts during periods of intense precipitation. In addition, recent evidence suggests that using accurate soil moisture boundary conditions to initialize weather prediction models may improve the accuracy of long-term precipitation forecasts.
Consequently, an ongoing challenge for hydrologists and remote sensing scientistsis the design of a soil moisture observing system to operationally monitor soil moisture over continental-scale regions. Two approaches are currently feasible. The first is to emphasize the observation of rainfall and use a land surface hydrology model to predict soil moisture based on precipitation measurements. The second approach is to employ relatively new remote sensing technologies that allow for the retrieval of surface soil moisture from microwave observations of the land surface. Unfortunately, both approaches have shortcomings. Hydrologic models are difficult to calibrate and require high quality forcing data. Likewise, remote sensing observations of soil moisture are available only for shallow portions of the soil column (0 to 5-cm) and exhibit large errors in heavily vegetated areas. Given the inherent limitations in each approach, recent work has focused on the development of data assimilation strategies to integrate modeling and remote sensing approaches. Data assimilation systems are typically designed to merge uncertain predictions from models with incomplete and noisy measurements from an observing system. Ideally, assimilation approaches optimally combine model predictions and independent observations in such a manner that the shortcomings of each approach are mutually compensated.
This particular example - based on data collected over Oklahoma - uses an Ensemble Kalman filter data assimilation strategy to correct land surface model predictions for errors arising from the use of poor rainfall forcing data. If properly interpreted, independent measurements of near-surface soil moisture provide a means of detecting the presence of rainfall error. The Ensemble Kalman filter inputs vertically shallow (0- to 5-cm) observations of soil moisture and updates model state predictions based on these observations. Most importantly, it uses the physics of the hydrologic model to vertically extrapolate surface measurements to deeper soil states not directly observed by the remote sensor. In this way, the model is used to compensate for the vertical limitations of the observations and the observations used to correct model errors arising from poor rainfall forcing.